The efficient operation of many applications in present computing environments depends upon fast, powerful and flexible computing systems. The configuration and design of such systems has become very complicated when such systems are to be used in an "enterprise" commercial environment where there may be many separate departments, many different problem types and continually changing computing needs. Users in such environments generally want to be able to quickly and easily change the capacity of the system, its speed and its configuration. They may also want to expand the system work capacity and change configurations to achieve better utilization of resources without stopping execution of application programs on the system. In addition they may want be able to configure the system in order to maximize resource availability so that each application will have an optimum computing configuration.
Traditionally, computing speed has been addressed by using a "shared nothing" computing architecture where data, business logic, and graphic user interfaces are distinct tiers and have specific computing resources dedicated to each tier. Initially, a single central processing unit was used and the power and speed of such a computing system was increased by increasing the clock rate of the single central processing unit. More recently, computing systems have been developed which use several processors working as a team instead one massive processor working alone. In this manner, a complex application can be distributed among many processors instead of waiting to be executed by a single processor. Such systems typically consist of several central processing units (CPUs) which are controlled by a single operating system. In a variant of a multiple processor system called "symmetric multiprocessing" or SMP, the applications are distributed equally across all processors. The processors also share memory. In another variant called "asymmetric multiprocessing" or AMP, one processor acts as a "master" and all of the other processors act as "slaves." Therefore, all operations, including the operating system, must pass through the master before being passed onto the slave processors. These multiprocessing architectures have the advantage that performance can be increased by adding additional processors, but suffer from the disadvantage that the software running on such systems must be carefully written to take advantage of the multiple processors and it is difficult to scale the software as the number of processors increases. Current commercial workloads do not scale well beyond 8-24 CPUs as a single SMP system, the exact number depending upon platform, operating system and application mix.
For increased performance, another typical answer has been to dedicate computer resources (machines) to an application in order to optimally tune the machine resources to the application. However, this approach has not been adopted by the majority of users because most sites have many applications and separate databases developed by different vendors. Therefore, it is difficult, and expensive, to dedicate resources among all of the applications especially in environments where the application mix is constantly changing.
Alternatively, a computing system can be partitioned with hardware to make a subset of the resources on a computer available to a specific application. This approach avoids dedicating the resources permanently since the partitions can be changed, but still leaves issues concerning performance improvements by means of load balancing of resources among partitions and resource availability.
The availability and maintainability issues were addressed by a "shared everything" model in which a large centralized robust server that contains most of the resources is networked with and services many small, uncomplicated client network computers. Alternatively, "clusters" are used in which each system or "node" has its own memory and is controlled by its own operating system. The systems interact by sharing disks and passing messages among themselves via some type of communications network. A cluster system has the advantage that additional systems can easily be added to a cluster. However, networks and clusters suffer from a lack of shared memory and from limited interconnect bandwidth which places limitations on performance.
In many enterprise computing environments, it is clear that the two separate computing models must be simultaneously accommodated and each model optimized. Several prior art approaches have been used to attempt this accommodation. For example, a design called a "virtual machine" or VM developed and marketed by International Business Machines Corporation, Armonk, New York, uses a single physical machine, with one or more physical processors, in combination with software which simulates multiple virtual machines. Each of those virtual machines has, in principle, access to all the physical resources of the underlying real computer. The assignment of resources to each virtual machine is controlled by a program called a "hypervisor". There is only one hypervisor in the system and it is responsible for all the physical resources. Consequently, the hypervisor, not the other operating systems, deals with the allocation of physical hardware. The hypervisor intercepts requests for resources from the other operating systems and deals with the requests in a globally-correct way.
The VM architecture supports the concept of a "logical partition" or LPAR. Each LPAR contains some of the available physical CPUs and resources which are logically assigned to the partition. The same resources can be assigned to more than one partition. LPARs are set up by an administrator statically, but can respond to changes in load dynamically, and without rebooting, in several ways. For example, if two logical partitions, each containing ten CPUs, are shared on a physical system containing ten physical CPUs, and, if the logical ten CPU partitions have complementary peak loads, each partition can take over the entire physical ten CPU system as the workload shifts without a re-boot or operator intervention.
In addition, the CPUs logically assigned to each partition can be turned "on" and "off" dynamically via normal operating system operator commands without re-boot. The only limitation is that the number of CPUs active at system intitialization is the maximum number of CPUs that can be turned "on" in any partition.
Finally, in cases where the aggregate workload demand of all partitions is more than can be delivered by the physical system, LPAR weights can be used to define how much of the total CPU resources is given to each partition. These weights can be changed by operators on-the-fly with no disruption.
Another prior art system is called a "Parallel Sysplex" and is also marketed and developed by the International Business Machines Corporation. This architecture consists of a set of computers that are clustered via a hardware entity called a "coupling facility" attached to each CPU. The coupling facilities on each node are connected via a fiber-optic link and each node operates as a traditional SMP machine, with a maximum of 10 CPUs. Certain CPU instructions directly invoke the coupling facility. For example, a node registers a data structure with the coupling facility, then the coupling facility takes care of keeping the data structures coherent within the local memory of each node.
The Enterprise 10000 Unix server developed and marketed by Sun Microsystems, Mountain View, Calif., uses a partitioning arrangement called "Dynamic System Domains" to logically divide the resources of a single physical server into multiple partitions, or domains, each of which operates as a stand-alone server. Each of the partitions has CPUs, memory and I/O hardware. Dynamic reconfiguration allows a system administrator to create, resize, or delete domains on the fly and without rebooting. Every domain remains logically isolated from any other domain in the system, isolating it completely from any software error or CPU, memory, or I/O error generated by any other domain. There is no sharing of resources between any of the domains.
The Hive Project conducted at Stanford University uses an architecture which is structured as a set of cells. When the system boots, each cell is assigned a range of nodes that it owns throughout execution. Each cell manages the processors, memory and I/O devices on those nodes as if it were an independent operating system. The cells cooperate to present the illusion of a single system to user- level processes.
Hive cells are not responsible for deciding how to divide their resources between local and remote requests. Each cell is responsible only for maintaining its internal resources and for optimizing performance within the resources it has been allocated. Global resource allocation is carried out by a user-level process called "wax." The Hive system attempts to prevent data corruption by using certain fault containment boundaries between the cells. In order to implement the tight sharing expected from a multiprocessor system despite the fault containment boundaries between cells, resource sharing is implemented through the cooperation of the various cell kernels, but the policy is implemented outside the kernels in the wax process. Both memory and processors can be shared.
A system called "Cellular IRIX" developed and marketed by Silicon Graphics Inc. Mountain View, Calif., supports modular computing by extending traditional symmetric multiprocessing systems. The Cellular IRIX architecture distributes global kernel text and data into optimized SMP-sized chunks or "cells". Cells represent a control domain consisting of one or more machine modules, where each module consists of processors, memory, and I/O. Applications running on these cells rely extensively on a full set of local operating system services, including local copies of operating system text and kernel data structures. Only one instance of the operating system exists on the entire system. Inter-cell coordination allows application images to directly and transparently utilize processing, memory and I/O resources from other cells without incurring the overhead of data copies or extra context switches.
Another existing architecture called NUMA-Q developed and marketed by Sequent Computer Systems, Inc., Beaverton, Oreg. uses "quads", or a group of four processors per portion of memory, as the basic building block for NUMA-Q SMP nodes. Adding I/O to each quad further improves performance. Therefore, the NUMA-Q architecture not only distributes physical memory but puts a predetermined number of processors and PCI slots next to each part. The memory in each quad is not local memory in the traditional sense. Rather, it is one third of the physical memory address space and has a specific address range. The address map is divided evenly over memory, with each quad containing a contiguous portion of address space. Only one copy of the operating system is running and, as in any SMP system, it resides in memory and runs processes without distinction and simultaneously on one or more processors.
Accordingly, while many attempts have been made at providing a flexible computer system having maximum resource availability and scalability, existing systems each have significant shortcomings. Therefore, it would be desirable to have a new computer system design which provides improved flexibility, resource availability and scalability. Furthermore, to allow the proper communication between different portions of the system, it would be desirable to have communications throughout the system that would allow information to be transmitted across any system boundary.